Contrary to the traditional clustering methods (often based on parametric models), a recently popular non-parametric method, spectral clustering (SC), employs eigendecomposition of pairwise similarities, and has been shown successful. Despite the advantages of spectral clustering, due to its computational and spatial complexity, its use in remote sensing applications is possible only through approximate spectral clustering (ASC), i.e. SC of the data representatives obtained by quantization or sampling. In this study, we show that, compared to other quantization methods, neural network (self-organizing map or neural gas) based quantization produces better quantization for ASC, to achieve high clustering accuracies.
TASDEMIR Kadim;
2011-08-29
Institute of Electrical and Electronics Engineers (IEEE)
JRC65263
https://publications.jrc.ec.europa.eu/repository/handle/JRC65263,
| Name | Country | City | Type |
|---|
This document is only visible at the Commission level.
You are not authorized to publish or distribute it outside the European Commission.
This is a public document. You can share this publication.
Datasets
| ID | Title | Public URL |
|---|
Dataset collections
| ID | Acronym | Title | Public URL |
|---|
Scripts / source codes
| Description | Public URL |
|---|
Additional supporting files
| File name | Description | File type |
|---|